Hearing is a cognitive capability that humans use to diagnose problems and troubleshoot issues that arise in a variety of environments. For example, a human may listen to the sounds of an automobile engine to diagnose a problem with the engine. In another example, a human may hear a beeping sound in an indoor environment and determine that a fire alarm has been activate. However, there is little to no automation in using sounds for diagnosing problems in Internet-of-Things (IoT) device contexts in which a device listens for audio in an environment. Using audio analysis for troubleshooting is problematic due to the fact that sounds in different contexts have very different characteristics.
A domain is a specific area, location, or context in which a set of unique sounds is characterized. For example, the sounds produced by a faulty car engine may be different from the sounds produced by a faulty industrial equipment engine or the sounds produced by a microwave. As a result, the most appropriate classification techniques are different in each domain, and any single classification scheme that works well in one domain may not work well in other domains. As a result, classifying received audio often does not function properly in different contexts and environments.
In general, machine learning approaches can be used to train and adapt behavior to different constraints. However, effective machine learning techniques require appropriate training data to be used for training in the appropriate context. Obtaining clean and curated data for training is one of the most expensive and time-consuming tasks of any machine learning system.